Finite-Sample Guarantees for High-Dimensional DML. (arXiv:2206.07386v1 [econ.EM])

Web: http://arxiv.org/abs/2206.07386

Debiased machine learning (DML) offers an attractive way to estimate
treatment effects in observational settings, where identification of causal
parameters requires a conditional independence or unconfoundedness assumption,
since it allows to control flexibly for a potentially very large number of
covariates. This paper gives novel finite-sample guarantees for joint inference
on high-dimensional DML, bounding how far the finite-sample distribution of the
estimator is from its asymptotic Gaussian approximation. These guarantees are
useful to applied researchers, as they are informative about …

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